update 传统检测优化过滤

AlgorithmModule/src/ALLImgCheckAnalysisy.cpp

@@ -420,7 +420,8 @@ int ALLImgCheckAnalysisy::InitData()
 
 int ALLImgCheckAnalysisy::Det_Product(std::shared_ptr<Product> &product)
 {
-
+	string cur_time_s = CheckUtil::getCurTimeHMS();
+	printf("[%s]>>>>>>>>>>>>>>>Det_Product****************det Start************\n", cur_time_s.c_str());
 	// 处理每个相机
 	while (true)
 	{
@@ -449,8 +450,8 @@ int ALLImgCheckAnalysisy::Det_Product(std::shared_ptr<Product> &product)
 	// AnalysiyAll(0);
 	SetProductResult(product);
 	product->SetCheckEnd();
-	string cur_time = CheckUtil::getCurTimeHMS();
+	string cur_time_e = CheckUtil::getCurTimeHMS();
-	printf("[%s]>>>>>>>>>>>>>>>Det_Product****************det End************\n", cur_time.c_str());
+	printf("[%s]>>>>>>>>>>>>>>>Det_Product****************det End************\n", cur_time_e.c_str());
 
 	return 0;
 }

AlgorithmModule/src/ImgCheckAnalysisy.cpp

@@ -465,9 +465,12 @@ int GetEdgeRoi(Mat img, Rect &new_roi, cv::RotatedRect &rotated_roi, float scale
 
 	// 二值化找最大连通域
     Mat r_img_bin;
-    threshold(r_img, r_img_bin, 15, 255, THRESH_BINARY);
+    threshold(r_img, r_img_bin, 35, 255, THRESH_BINARY);
+	// 做一步闭运算
+	Mat r_img_bin_close;
+	morphologyEx(r_img_bin, r_img_bin_close, MORPH_CLOSE, Mat::ones(15, 15, CV_8U));
     std::vector<std::vector<cv::Point>> contours;
-    cv::findContours(r_img_bin, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
+    cv::findContours(r_img_bin_close, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
     if (contours.empty())
     {
         return 2;
@@ -779,7 +782,6 @@ int ImgCheckAnalysisy::CheckRun()
 	{
 		m_pdetlog->bPrintStr = true;
 	}
-	m_pdetlog->bPrintStr = true;
 	
 	m_pdetlog->AddCheckstr(PrintLevel_0, "1、basic Info", "---------------------------1、basic Info---------------------------------");
 	m_pdetlog->AddCheckstr(PrintLevel_0, "Version", "%s", GetVersion().c_str());
@@ -2509,9 +2511,43 @@ int ImgCheckAnalysisy::BLobToDetResult()
 	long t1 = CheckUtil::getcurTime();
 	m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "BLobToDetResult", " Start old qx num  %ld", m_pDetResult->pQx_ErrorList->size());
 
+	// 传统检测：按 nAreaFilter（原图面积）+ nCountFilter（个数）直接卡控最终上报。
+	// 对 blobs.blobTab 按面积降序，过滤 area <= nAreaFilter 的 blob，再取前 nCountFilter 个。
+	// AI 路径（bOpen=false）不做此过滤，保持原有全量上报。
+	const bool bTcsCountFilter = (m_pbaseCheckFunction != nullptr && m_pbaseCheckFunction->traditionDet.bOpen);
+	const float fAreaFilter  = bTcsCountFilter ? m_pbaseCheckFunction->traditionDet.nAreaFilter  : 0.0f;
+	const int   nCountFilter = bTcsCountFilter ? m_pbaseCheckFunction->traditionDet.nCountFilter : blobs.blobCount;
+	int nFiltered = 0;
+
+	// 传统路径：构造按面积降序的索引，用于面积 + 个数卡控
+	std::vector<int> sortedIdx;
+	int nReport = blobs.blobCount; // 实际需要遍历上报的 blob 数量
+	if (bTcsCountFilter)
+	{
+		sortedIdx.resize(blobs.blobCount);
+		for (int j = 0; j < blobs.blobCount; j++) sortedIdx[j] = j;
+		std::sort(sortedIdx.begin(), sortedIdx.end(), [&](int a, int b) {
+			return blobs.blobTab[a].area > blobs.blobTab[b].area;
+		});
+
+		// 已按面积降序：第一个 area <= nAreaFilter 的 blob 即为有效区间终点
+		nReport = 0;
+		while (nReport < blobs.blobCount && blobs.blobTab[sortedIdx[nReport]].area > fAreaFilter)
+		{
+			nReport++;
+		}
+		if (nReport > nCountFilter)
+		{
+			nReport = nCountFilter;
+		}
+		nFiltered = blobs.blobCount - nReport;
+	}
+
 	// 遍历每个检测blob
-	for (int i = 0; i < blobs.blobCount; i++)
+	for (int idx = 0; idx < nReport; idx++)
 	{
+		const int i = bTcsCountFilter ? sortedIdx[idx] : idx;
+
 		cv::Rect roi;
 		roi.x = blobs.blobTab[i].minx;
 		roi.y = blobs.blobTab[i].miny;
@@ -2591,7 +2627,7 @@ int ImgCheckAnalysisy::BLobToDetResult()
 		}
 	}
 	long t2 = CheckUtil::getcurTime();
-	m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "BLobToDetResult", " end  qx num  %ld use time %ld", m_pDetResult->pQx_ErrorList->size(), t2 - t1);
+	m_pdetlog->AddCheckstr(PrintLevel_1, DET_LOG_LEVEL_3, "BLobToDetResult", " end  qx num  %ld filtered %d use time %ld", m_pDetResult->pQx_ErrorList->size(), nFiltered, t2 - t1);
 	return 0;
 }
 

TcsCheckModule/include/TcsCheck.h

@@ -130,6 +130,7 @@ private:
     void Process(bool bDraw = false);
 
     // 纯分类：对二值图做连通域分析+缺陷分类，结果写入 m_vecDefectInfo
+    // 面积/个数过滤已上移至 BLobToDetResult 统一处理，此处仅做缺陷类型打标签
     void ClassifyBlobs(const cv::Mat& blurCrop, const cv::Mat& imgBlob);
     // 纯绘制：基于 m_vecDefectInfo 绘制缺陷标注
     cv::Mat DrawBlobInfoImage(const cv::Mat& imgCrop, const cv::Mat& imgBlob);

TcsCheckModule/src/TcsCheck.cpp

@@ -240,31 +240,14 @@ void CTcsCheck::ClassifyBlobs(const cv::Mat& blurCrop, const cv::Mat& imgBlob)
 	// logStep("1.连通域分析");
 	if (nLabels <= 1) return;
 
-	struct BlobItem { int label; int area; };
+	// 大块缺陷的面积阈值（分类决策用，非过滤）
-	std::vector<BlobItem> blobs;
-	blobs.reserve(std::max(0, nLabels - 1));
-	for (int label = 1; label < nLabels; ++label) {
-		const int area = stats.at<int>(label, cv::CC_STAT_AREA);
-		if (area > m_cpCfg.nAreaFilter) {
-			blobs.push_back({ label, area });
-		}
-	}
-	// logStep("2.面积过滤");
-	if (blobs.empty()) return;
-
-	std::sort(blobs.begin(), blobs.end(), [](const BlobItem& a, const BlobItem& b) {
-		return a.area > b.area;
-	});
-	// logStep("3.面积排序");
-
-	// 大块缺陷的面积阈值
 	const int largeAreaThreshold = std::max(1, m_cpCfg.nBlockSize * m_cpCfg.nBlockSize / 4);
-	const int topK = std::min(m_cpCfg.nCountFilter, static_cast<int>(blobs.size()));
-
-	for (int i = 0; i < topK; ++i) {
-		const int label = blobs[i].label;
-		const int area = blobs[i].area;
 
+	// 面积/个数过滤已统一上移到 BLobToDetResult 处理，
+	// 此处对所有连通域逐一分类，仅做缺陷类型打标签。
+	for (int label = 1; label < nLabels; ++label) {
+		const int area = stats.at<int>(label, cv::CC_STAT_AREA);
+		if (area < 5) continue;
 		// bounding rect
 		const int x = stats.at<int>(label, cv::CC_STAT_LEFT);
 		const int y = stats.at<int>(label, cv::CC_STAT_TOP);